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Structured evidence from upstream sources boosts LLM repair accuracy by up to 23 percentage points, revolutionizing how we adapt to breaking changes in software dependencies.
AgentTether repairs over 65% of failures in complex LLM tasks without modifying the agent, revolutionizing how we ensure reliability in AI deployments.
Autonomous web agents get a serious upgrade with WebXSkill, which lets them learn and execute skills with both code-level precision and human-readable guidance.
Stop wasting time on manual LLM domain adaptation: AutoAdapt automates the process and boosts accuracy by 25% over existing AutoML methods.
Forget hand-crafted benchmarks: this paper shows how LLMs can continuously generate relevant evaluation datasets for enterprise AI agents from just a few semi-structured documents.
